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Principal component analysis (PCA) is widely used in data processing and dimensionality reduction. However, PCA suffers from the fact that each principal component is a linear combination of all the original variables, thus it is often difficult to interpret the results. We introduce a new method called sparse principal component analysis (SPCA) using the lasso (elastic net) to produce modified principal components with sparse loadings. We first show that PCA can be formulated as a regression-type optimization problem; sparse loadings are then obtained by imposing the lasso (elastic net) constraint on the regression coefficients. Efficient algorithms are proposed to fit our SPCA models for both regular multivariate data and gene expression arrays. We also give a new formula to compute the total variance of modified principal components. As illustrations, SPCA is applied to real and simulated data with encouraging results. [ABSTRACT FROM AUTHOR]

Discusses an alternative algorithm in which the smooth functions are estimated using smoothing spline. The generalized cross-validation (GSV) based techniques used to select the smoothing parameters and number of terms; Structure of the automatic smoothing spline projection pursuit (ASP) algorithm; Examples comparing ASP with the standard supersmoother-based PPR algorithm of Friedman.